The Parallel Distributed Image Search Engine (ParaDISE)
It provides a flexible and scalable platform for image retrieval, particularly in medical contexts, but is incremental as it integrates existing techniques.
The paper introduces ParaDISE, an open-source image retrieval system that combines visual and text-based search to address challenges in modeling high-level concepts, with applications demonstrated in medical literature retrieval and feature evaluation.
Image retrieval is a complex task that differs according to the context and the user requirements in any specific field, for example in a medical environment. Search by text is often not possible or optimal and retrieval by the visual content does not always succeed in modelling high-level concepts that a user is looking for. Modern image retrieval techniques consist of multiple steps and aim to retrieve information from large--scale datasets and not only based on global image appearance but local features and if possible in a connection between visual features and text or semantics. This paper presents the Parallel Distributed Image Search Engine (ParaDISE), an image retrieval system that combines visual search with text--based retrieval and that is available as open source and free of charge. The main design concepts of ParaDISE are flexibility, expandability, scalability and interoperability. These concepts constitute the system, able to be used both in real-world applications and as an image retrieval research platform. Apart from the architecture and the implementation of the system, two use cases are described, an application of ParaDISE in retrieval of images from the medical literature and a visual feature evaluation for medical image retrieval. Future steps include the creation of an open source community that will contribute and expand this platform based on the existing parts.